Method and system for providing soil analysis

ABSTRACT

The present disclosure describes a system, method, and non-transitory computer readable medium for analyzing soil samples. Accordingly, soil sample units may be obtained and provided to a server that generates raw data. The raw data is subsequently organized into a sub-report for each nutrient or variable contained in the raw data. An average for each nutrient in the raw data and a number of additional factors related to the raw data may be calculated. The average and additional factors are used to determine bulk recommendations by comparing target data to an exchangeable measured value. Additionally, the factors are also used to determine challenges and solutions by comparing the average data to the target data for each nutrient. The system compares the raw data to the measured values an mathematically adjusts the compared values to compute an optimal treatment algorithm.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a § 371 National Stage Application of application ofPCT/US15/52947 filed Sep. 29, 2015 and U.S. provisional patentapplication Ser. No. 62/056,757 filed Sep. 29, 2014 under 35 U.S.C. §111(a) (hereby specifically incorporated herein by reference).

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

None.

REFERENCE TO SEQUENCE LISTING, A TABLE FOR A COMPUTER PROGRAM LISTING,COMPACT DISC APPENDIX

None.

BACKGROUND OF THE INVENTION

The current technology relates to providing a consistent and accuratesoil analysis for increasing crop yield.

BRIEF SUMMARY OF THE INVENTION

The present technology relates to a system and method for analyzing soilsamples to determine appropriate treatments. An embodiment of thepresent technology includes a method for generating a recommendation toincrease yield for an agricultural crop. That method comprises, in part,receiving, by one or more processors, a total measurement of a nutrientcontained in a soil sample. The one or more processors also receive anestimate of an amount of the nutrient available in solution to beabsorbed by roots of the agricultural crop from the soil sample. The oneor more processors also receive a type of the agricultural crop. The oneor more processors select, from a plurality of threshold values a firstthreshold value for the total measurement and a second threshold valuefor the estimate based on the type of the agricultural crop.

The one or more processors compare the total measurement to the firstthreshold value and compare the estimate to the second threshold value.The one or more processors generate a combination recommendation toincrease yield for the agricultural crop type based on the comparisonsand providing, by the one or more processors, the combinationrecommendation for display. In one embodiment, when the totalmeasurement of the nutrient is less than the first threshold value,generating the combination recommendation includes generating arecommendation to add a foliar fertilizer. When the estimate is lessthan the first threshold value, generating the combinationrecommendation includes generating a recommendation to add a soilfertilizer. In another embodiment, the nutrient is an anion and may beselected from the group consisting of phosphorus (P); sulfur (S);chloride (Cl) and bicarbonate (HCO₃). The nutrient may also be selectedfrom the group consisting of a base cation, nitrogen, and amicronutrient. In other embodiments, the measurement, and/or estimatemay be received from a sensor system.

In another embodiment, the one or more processors in the method mayreceive a second total measurement of a second nutrient contained in thesoil sample, and may also receive a second estimate of an amount of thesecond nutrient available to be absorbed by the roots of theagricultural crop. In this embodiment, generating the combinationrecommendation is further based on the second total measurement and thesecond estimate.

In another embodiment, the method may also comprise selecting from theplurality of threshold values a third threshold value for the secondtotal measurement and a fourth threshold value for the second estimate.In yet another embodiment, generating the combination recommendation isfurther based on a comparison of the third threshold value to the secondtotal measurement and a comparison of the fourth threshold value to thesecond estimate. The combination recommendation may also include aspecific recommendation for each of the total measurement, the estimate,the second total measurement, and the second estimate.

In another embodiment, the methods may also include ranking the specificrecommendations based on predetermined ranking priorities for theagricultural crop, and generating the combination recommendation isfurther based on the ranking. The method may also comprise selecting thepredetermined ranking priorities from a set of predetermined rankingpriorities based on the agricultural crop type. Additionally, eachthreshold value of the plurality of threshold values may be associatedwith a particular agricultural crop type such that the plurality ofthreshold values corresponds to a plurality of different agriculturalcrop types.

The present technology may also include a system for generating arecommendation to increase yield for an agricultural crop. Among otherthings, the system comprises a memory storing a plurality of thresholdvalues and one or more computing devices having one or more processors.The one or more processors may be configured to receive a totalmeasurement of a nutrient contained in a soil sample, receive anestimate of an amount of the nutrient available to be absorbed by rootsof the agricultural crop from the soil sample, and receive a type of theagricultural crop.

The one or more processors are also configured to select from theplurality of threshold values a first threshold value for the totalmeasurement and a second threshold value for the estimate based on thetype of the agricultural crop. The one or more processors are alsospecifically configured to compare the total measurement to the firstthreshold value and compare the estimate to the second threshold value.The one or more processors may be further configured to generate acombination recommendation to increase yield for the agricultural croptype based on the comparison, and provide the combination recommendationfor display.

The one or more processors of the system are further configured togenerate the combination recommendation by generating a recommendationto add a foliar fertilizer, when the total measurement of the nutrientis less than the first threshold value.

The one or more processors are further configured to generate thecombination recommendation by generating a recommendation to add a soilfertilizer, when the estimate is less than the first threshold value.

In another embodiment, the nutrient or nutrients being analyzed may bean anion. That anion may be one of phosphorus (P); sulfur (S); chloride(Cl) and/or bicarbonate (HCO₃). The nutrient may also be a base cation,nitrogen, and/or a micronutrient.

The one or more processors of the present technology may further beconfigured to receive the total measurement and estimate from a sensorsystem.

In another embodiment, the one or more processors may be furtherconfigured to receive a second total measurement of a second solublenutrient contained in the soil sample, and receive a second estimate ofan amount of the second nutrient available to be absorbed by the rootsof the agricultural crop. In such an embodiment, the one or moreprocessors may be configured to generate the combination recommendationfurther based on the second total measurement and the second estimate.

The one or more processors may also be configured to select rom theplurality of threshold values a third threshold value for the secondtotal measurement and a fourth threshold value for the second estimate.The one or more processors may also be configured to generate thecombination recommendation, wherein the recommendation is further basedon a comparison of the third threshold value to the second totalmeasurement and a comparison of the fourth threshold value to the secondestimate. The combination recommendation includes a specificrecommendation for each of the total measurement, the estimate, thesecond total measurement, and the second estimate.

The one or more processors may further be configured to rank thespecific recommendations based on predetermined ranking priorities forthe agricultural crop, and generate the combination recommendationfurther based on the ranking. The one or more processors are furtherconfigured to select the predetermined ranking priorities from a set ofpredetermined ranking priorities based on the agricultural crop type.The one or more processors are further configured to associate eachthreshold value of the plurality of threshold values with a particularagricultural crop type such that the plurality of threshold valuescorrespond to a plurality of different agricultural crop types.

According to one example, the present disclosure describes a system foranalyzing soil samples. The system may include physical sample units tocollect, store and transport soil samples. The soil samples may beprepared with a test, such as the Albrecht test and/or a waterextractable test. The raw data from these tests may be entered into aserver that processes and/or stores the raw data, such as a list ofnutrients and the quantities thereof. Once the raw data is generated, itmay be sent to a database. The database may be stored in the firstserver, or at a location remote from the server.

Another server may download the raw data from the first server or thedatabase to generate a sub-report for each nutrient in the raw data. Theserver may be of any type including a stand-alone server or a serverlocated in a server farm or data center. The server may be one or moreprocessors. The server then calculates the average for each nutrient inthe raw data and calculates a number of factors related to the raw data.For example, the number of factors may include target data for eachnutrient, an estimated nitrogen release (ENR), and an estimatedphosphorus release (EPR), based on the raw data from the plurality ofsoil samples. These factors are used to determine bulk recommendationsby comparing target data to an exchangeable measured value.Additionally, the servers are configured to calculate and predictsolutions by comparing the average or actual data to the target data foreach nutrient.

In other examples, the factors may be used to determine an anion ratioby comparing a first ratio to an optimal ratio and a cation ratio bydetermining a second ratio of each nutrient compared to other nutrientsin the sample. Based on the above determinations, the server may providea treatment recommendation.

According to another example, the present disclosure describes a methodfor providing soil analysis that includes receiving several soilsamples. The method generates raw data from the soil samples using atleast one test. This test may include an Albrecht test or a waterextractable (solubility) test. Further, the raw data may include a listof nutrients and the quantities thereof in each of the several soilsamples. The list of nutrients may include, but is not limited tocalcium (Ca), magnesium (Mg), potassium (K), sodium (Na), phosphorus(P), sulfur (S), chloride (Cl), bicarbonate (HCO3), nitrate (NO3),ammonium (NH4), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), boron(B) and/or silicon (Si). The raw data is subsequently entered into adatabase.

The raw data is then downloaded from the database to another serverwhere a sub-report may be generated for each nutrient in the raw data.Using the raw data, a number of factors, including the average of eachnutrient, target or threshold data for each nutrient, an estimatednitrogen release (ENR), and an estimated phosphorus release (EPR), arecalculated for each of the soil samples. These data points can be usedby the one or more processors of the present technology to provide bulkand foliar treatment recommendations to increase crop yield.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of the system for providing soil analysis;

FIG. 2 shows a flowchart for analyzing the soil analysis;

FIGS. 3A and 3B illustrate a table of the nutrients and variablesevaluated during the soil analysis;

FIG. 4 illustrates a chart of nitrogen's influence on tissue content;

FIGS. 5A and 5B show the saturation index and the influence saturationindex has on corn yield;

FIGS. 6A and 6B illustrates an example of the solubility of calcium;

FIGS. 7A-7C illustrates a chart of available magnesium versus yield on avariety of crops;

FIGS. 8A-8C shows a chart of available zinc versus yield for a varietyof crops;

FIGS. 9A-9C illustrates charge balance versus yield for several types ofcrops; and

FIGS. 10A-10C illustrates a chart showing saturation index versus yieldfor different crop varietals;

FIG. 11(a) illustrates a page from a display provided by the system;

FIG. 11(b) illustrates a page from a display provided by the system; and

FIG. 11(c) illustrates a page from a display provided by the system.

DETAILED DESCRIPTION

One of the objectives of the current technology is to provide aconsistent and accurate soil analysis for increasing crop yield orimproving turf quality.

The present disclosure begins with providing users with submission formsand sample containers to obtain a plurality of samples. However, themethod of soil collection may be of any type known to those of skill inthe art. In this regard, samples may be collected from farms, sportsvenues, home lawns, etc., using the supplied submission forms and samplecontainers. The collected samples may then be submitted to a laboratoryto generate raw data with regard to the collected soil samples. In thisregard, laboratory personnel may prepare the samples using any of avariety of devices and/or well-known tests, such as the traditionalAlbrecht test (also known as “Exchangeable” or “Total”) or the waterextractable test (also known as “soluble paste” or “Available”). Thelaboratory equipment may generate a list of the nutrients and variablesevaluated that may be included in a final report. The laboratoryequipment may include one or more sensor systems for detecting one ormore of the nutrients described herein. These nutrients will bediscussed in greater detail below with respect to FIGS. 3A and 3B.

The laboratory may enter the raw data into a server or database. Thesystem and method of the present technology may subsequently downloadthe data to another server or set of servers. The raw data is thencompiled, processed, and analyzed to generate information regarding thesoil type, nutrient content, charge balance, saturation index, etc., ofthe soil samples. This information is compared to target information forthe soil based on the location, climate, and expected (target) results.This target data may be stored on the system servers or networkaccessible databases. The system may then generate reports providingdetailed information about the soil type, nutrient content, chargebalance, saturation index, etc., which may be sent to the individualthat submitted the samples. Additionally, the system may providerecommendations and custom-tailored products based on the generatedreport to improve the overall quality of the soil. The recommendationsmay be based on correlations between crop yield or turf quality and aspecific nutrient content.

Turning to FIG. 1, a system 100 for providing a consistent and accuratesoil analysis is shown. The system 100 includes a farm 110, at least onesoil sample 120, a first server 130, a database 140, a network 150, asecond server 160, and a treatment course 170.

The farm 110 may include a number of fields, each with a different crop.Accordingly, a farmer may take several soil samples from each fieldusing supplied containers. One of ordinary skill in the art wouldrecognize that the farmer may take several samples from differentlocations and/or at different depths of the same field. Alternatively,the farmer may take several samples from different fields. Further,while FIG. 1 illustrates the farm 110, one of ordinary skill in the artwould recognize that the soil samples may be collected from a variety oflocations, such as athletic fields (i.e., baseball, football, tennis),golf courses, homes (i.e. gardens and lawn), etc.

The soil sample 120 may be collected in any type of container thatallows the farmer to collect at least one soil sample and transmit itfor further processing. In this regard, the at least one soil sample 120may include a bag or container, such as a vile or series of bottles,with soil samples as collected above.

The first server 130 may be operated by a laboratory or other facilitythat can conduct basic soil analysis to generate raw data with regard tothe collected soil samples. In this regard, the first server 130 mayinclude at least one processor, at least one memory, and laboratoryequipment for measuring the soil parameters listed in FIGS. 3A and 3B.The processor and memory are in communication with one another. Further,the first server may include a plurality of servers or automatedlaboratory equipment.

The database 140 may be used to store the raw data generated by thefirst server 130. In this regard, the database 140 may include a table,SQL database, or any other known storage technique. Additionally, thedatabase 140 may be located at the same facility as the first server130. Alternatively, the database 140 may be accessed by the first server130 via the network 150. In some examples, the database 140 may beco-located with the second server 160.

The network 150 may include any type of interconnected computer systemthat allows at least two devices to communicate with each other, such asa local area network (LAN), a wide area network (WAN), Ethernet, or theInternet. Additionally, the network 150 may be wired or wireless.

The second server 160 may be a soil analysis system. In this regard, thesecond server 160 may include at least one processor, at least onememory, and additional instructions and/or hardware for analyzing anddownloading the raw data stored in the database 140. In anotherembodiment, the second server may be configured to automaticallydownload the raw data from the first server.

Although the first server 130 and the second server 160 are described asseparate systems capable of performing their own operations, one ofordinary skill in the art would recognize that the first server 130 andthe second server 160 may be located in the same location.Alternatively, the functions of the first server 130 and the secondserver 160 may be performed by the same machine or cluster of servers.

The treatment course 170 may include a report containing the content ofthe soil sample and recommendations for improving the nutrient contentof the soil sample based on the soil analysis performed by the server160. Alternatively, the treatment course 170 may include a generated mixof chemicals for improving the nutrient content of the soil, includingfertilizers, surfactants, oxidizers, etc. Additionally, the secondserver 160 may provide both the report and the generated mix ofchemicals. The treatment course 170, may also include a system forautomatically applying a generated mix of chemicals for improving thenutrient content of the soil.

FIG. 2 illustrates a flowchart for providing a consistent and accuratesoil analysis. As noted above, a user, such as a farmer or fieldsuperintendent, obtains at least one soil sample. The soil sample isprovided to a laboratory to generate raw data with regard to thecollected soil samples. The raw data may be generated by preparing thesoil samples using a variety of devices, tests and techniques, such asan Albrecht test or a water extractable test. Albrecht Method, maypreferably include the “Ammonium Acetate,” “Bray” and “Olsen”procedures. The raw data may include a list of nutrients, such ascalcium, magnesium, potassium, and phosphorus, and the quantitiesthereof contained in the soil sample.

The raw data generated by the tests may be entered into a file, which isstored in a non-transitory computer-readable medium, such as a database,a hard-drive, memory card, flash drive, ROM, RAM, DRAM, DVD or otheroptical disks, as well as other write-capable and read-only memories.From there, the file may be furthered processed in one or moreprocessors, such as any conventional processor including multipleprocessors, multi-core processors, or a combination thereof, a dedicatedcontroller, such as an application specific integrated circuit (ASIC),field programmable gate array (FPGA), etc. The processor may becontained in a server. The processor may be configured to analyze thefile based on the raw data included in the report, such as the watercontent, the soil content, and the tissue content. The water content maybe saved as a document to be provided to the customer. Additionally, thetissue content is reported to the customer for their reports. The soilcontent contained in the file may be subjected to additional soilprocessing.

When the file is subjected to additional soil processing, the processormay be configured to download the raw data from the database foradditional processing. Accordingly, the server may then generate atleast one sub-report by separating each variable (e.g., nutrients,organic matter, type of soil, etc.) in the raw data for each of theplurality of soil samples into sub-reports. For example, all sampleswith organic matter greater than 10 may have their analysis runindependently of other samples. In another example, the samples may beseparated into groups according to the following average deviation andin the following order:

pH: 1

organic matter: 0.75

exchangeable calcium: 400

In this regard, the samples may be separated into the smallest possiblenumber of groups, such that the difference between the largest sampleand the smallest sample is the smallest.

Next, the server may calculate averages for each sub-report. That is,the server may take the raw data and calculate the average for eachnutrient in the soil samples. For example, if five samples from afive-acre corn field are submitted, the server will process all fivesamples to determine the average of each nutrient content across allfive samples. Accordingly, the system may determine the average ofnutrients such as Ca, Mg, K, Fe, P, etc. from the five samples takenfrom the five acre corn field.

Next the system may calculate target levels for each of the nutrients.That is, the system may determine what the optimum nutrient levelsshould be for a particular crop or turf type. The targets may be staticnumbers; however, some may be calculated on a sliding scale according tothe averages. For example P, Ca, Mg, K, and Fe may be calculated on asliding scale to give a realistic improvement goal recalculated by theservers in order to determine optimal yield. Additionally, certaintargets may be set by other factors, such as the type of soil or the pH.

Additionally, the server may calculate an estimated nitrogen release(ENR) and/or an estimated phosphorus release (EPR). An ENR is acalculated estimate of how much nitrogen may be organically or naturallyreleased into solution through a growing season. Similarly, EPR is acalculated estimate of how much phosphorus may be related through agrowing season. These calculations may be used to help supplementtreatment options based on the estimated loss of both nitrogen andphosphorus throughout the growing season.

After the content of the soil is determined based on the foregoingcalculations, the server may calculate bulk recommendations based on thevariables that effect the solubility of the nutrients. For example, theserver may generate an estimation of bulk treatments to apply to thesoil. Recommendations may be given, for example, for Ca, Mg, K, and Ptaking into account the target against the exchangeable measured valuescalculated above. In some examples, the Ca calculations may also takeinto account the sulfates and bicarbonates measured. Additionally, thecalculations above may also take into account the ratio of Ca to Mg, K,and Na. In further examples, calculations related to Mg may also takeinto account the bicarbonates found in the soil. In another example, theK calculations may also take into account the Na found in the soilsamples.

Next, the server may be configured to calculate challenges andsolutions. For instance, each average may be given a ranking (e.g. low,optimal, or high) based on its difference from the target. For example,challenges may indicate low nutrients, poor soil quality, off-balancepH, etc. Additionally, the challenges and solutions may indicatedeficient or excessive parameters. Accordingly, the ranking (or groupsof rankings) may be used to determine if a challenge is applicable. Thechallenges may be given an urgency ranking (e.g., high, medium, or low).According to this example, the challenges may be sorted by urgency withthe top challenges being listed in a report generated for the customer.

The server may also calculate anion ratios for the soil samples. Theanion ratios may be the comparison between HCO₃, NO₃, PO₄, SO₄, and Cl.The anion ratios may be converted to a percentage and compared tooptimal ratios stored on the server in order to determine thedifferences between the soil samples and the optimal soil content. Thishelps to provide recommendations for the user to improve their soilquality, thereby improving their yield. With respect to anions, thepresent systems and methods are configured to measure and analyze thevailable anion concentration, as well as determine if detrimental anionsare present. For example, Phosphorus in the form of P or PO₄, Sulfur asSO₄ and N as NO₃ are known to be beneficial. Accordingly, if thebeneficial anions are present, but deficient relative to the target forthe particular crop, the system will make a recommendation to supplementthose nutrients, either foliarly, or through the soil.

In contrast, anions such as chloride in the form of Cl and bicarbonateare known to be detrimental. Accordingly, if these detrimental anionsare present in levels in excess of the target for a particular crop, thesystem generates a recommendation to remediate the excess. Remediationrecommendations include but are not limited to stopping the input,adding a wetting agent to flush the anion, or adding a quantity ofadditional beneficial nutrients to offset the deleterious effects of thedetrimental anion.

In addition to anion ratios, the server may also calculate cation ratiosfrom the raw data. The cation ratios may be the relationship between Ca,Mg, K, Na, and NH₄. In this regard, the total ratios may simply bereported as PBS values. The available cation ratios may be converted toa percentage and reported to the client on the report. Additionally, thePBS may be to the client as a percentage compared to the optimal cationratios.

Using the calculations above, the server may generate a report on adisplay. The report may indicate the soil content (e.g. nutrients, typeof soil, pH balance) of the soil samples. Additionally, the report maycontain the ratio of each nutrient in relation to other nutrients. Thisis an important consideration since trying to replace one nutrient mayhave an effect on other nutrients in the soil. Additionally, the reportmay include recommendations for improving soil content.

Turning to FIGS. 3A and 3B, a table of the nutrients and variablesevaluated during the soil analysis process described above with respectto FIG. 2. For example, traditional systems compared nutrients and/orvariables to Albrecht's standards. In contrast, the exemplary system andmethod described herein compare the nutrients and/or variables tospecific crops and/or turf. In this regard, the system and methodsdescribed herein taken into account a myriad of additional factors, suchas saturation index, electrical conductivity, water solubility, soiltype, crop type, climate, latitude, longitude, soil pH, etc.

Additionally, the tables shown in FIGS. 3A and 3B illustrate the use ofa soluble paste test. The soluble paste test may also be used toestablish yield correlations. Additionally, the system and methoddescribed herein may use a silica extraction test to help improve yieldcorrelations.

Referring to FIG. 4, a graph showing the influence of available soilnitrogen on tissue content. That is, FIG. 4 shows the maximum nitrogencontent in grass tissue is maximum when the “available total N” in thesoil is between 40 and 50 ppm. This allows users to minimize N run-offinto waterways while reducing costs by eliminating over fertilizing.

FIG. 5A shows a determination of the saturation index as it correlatesto organic matter (OM) and cation exchange capacity (CEC). Thesaturation index is a unique parameter that gauges the soil's ability toallow water to drain. A lower number may mean the soil type is more likesand, while a high number represents a heavy or high clay soil. In thisregard, the saturation index increases as OM and CEC increase, whichallows a user to better predict and adjust his soil through tilling,aeration, wetting agents, etc.

FIG. 5B illustrates the effect that determining the optimal saturationindex for a crop has on its yield. According to this example, thesaturation index for a field of corn was determined. As shown in FIG.5B, the optimum saturation index for corn should be between 0.95 and1.0. As noted above, determining the saturation index allows a user tobetter predict and adjust the soil through any physical or chemicaltechnique known to those in the art.

FIG. 6 illustrates a chart comparing the solubility of calcium asassumed by Mehlich and the solubility of calcium as determined by themethods and system described herein. In this regard, Mehlich Testingassumes solubility is constant for each nutrient. As shown, Mehlichassumed that 10% of Ca would be solubilized regardless of the Ca contentof the soil. In contrast, in the present technology the percentage of Cacapable of being solubilized decreases as the parts per million of Ca inthe soil increases. Thus, it appears that Ca solubility is directlyrelated to the Ca content in the soil. In this regard, Ca solubilityappears to plateau when the Ca in the soil reaches approximately 2000ppm.

FIG. 7 shows the effect magnesium has on soybeans, wheat, and corn.Overall, magnesium has a negative effect on growth. For example,increased Mg levels reduced soybean yield by as much as 23%. Further,increased levels of Mg resulted in up to a 33% reduction in wheat yield.Additionally, corn yield was reduced by as much as 13%. In this regard,magnesium must remain below 2 meq/L. Thus, this information is factoredinto the system when determining the treatment recommendation.

FIG. 8 shows the effect zinc has on crop growth, in particular corn,soybeans, and wheat. In stark contrast to the increased Mg levelsdiscussed with regard to FIG. 7, FIG. 8 shows increased levels of Znimproved yield for corn, soybeans, and, most significantly, wheat.Therefore, considering increased levels of Zn may be considered fordetermining the treatment recommendation.

FIG. 9 illustrates the effect of charge balance on crop yield forsoybeans, wheat, and corn. In this regard, soybeans were shown to prefera slightly anionic soil. In contrast, what was shown to prefer aslightly cationic soil corn is shown as having a preference for aneutral soil. This information may be helpful in providing a treatmentrecommendation. That is, the knowing charge balance of the soil and thecrop being grown, the system described herein may provide arecommendation to improve yield based on the charge balance preferenceof the crops.

FIG. 10 shows the effect of saturation index on crop yield. While thegraphs for each crop have a different shape, all the graphs show thatcrops prefer a balanced soil. For example, a saturation index between0.95 to 1.0 having both capillary space and air space was shown to beideal for improving yield for corn, wheat, and soybeans. Heavy soilswere shown to be detrimental. Thus, the system and method describedherein may factor in the saturation index and the type of crop tooptimize crop yield.

The processors and servers of the present technology utilize all or someof the data provided in FIGS. 3A-11(c) to calculate potential yields andoptimal nutrient values for the potential yields of various agriculturalcrops. The processors and servers are configured to calculate treatmentrecommendations based on mathematical manipulations of the raw data.When raw data received from upstream servers in the system is comparedto known values of a given parameter, the processors and servers canadjust the raw data values in comparison to the known values in order todetermine an optimal treatment protocol. The treatment protocol may bebased on one two or any combination of data variables. Exemplary datavariables can be found in the attached figures. However, the presenttechnology may also rely on other data to compute soil treatmentrecommendations.

In a preferred embodiment, one or more soil samples are taken andanalyzed by a system as described herein. The soil sample may bespecific to a specific agricultural crop type (e.g. example, corn,almonds, wheat, avocado and soy). However, the present technology is notlimited to a specific type of crops, and multiple crops may be analyzedsimultaneously.

After the soil sample is obtained, the nutrient levels in the soil aremeasured and analyzed by one or more processors. At least two types ofnutrient analyses are performed. First, the total amount of nutrients inthe soil sample is measured. This measurement is taken in accordancewith the methods described herein. However, this total measurement doesnot assess how much of a given nutrient is actually available for theroots of a specific crop type to absorb. Accordingly, the one or moreprocessors of the present technology also estimate the amount of thespecific nutrient that is available in solution to be absorbed by theroots of the specific crop type. This estimation is based, at least inpart, on the results of a solubility test, as described above. After thetotal measurement and estimated nutrient solubility data are received bythe one or more processors, the system receives the specificagricultural crop type being analyzed. Specific crop types may be storedin a memory of the system. The system is also configured to analyze atleast one nutrient per agricultural crop type, and in preferredembodiments, can analyze a plurality of nutrients per agricultural croptype.

For each specific agricultural crop type, a threshold value is providedfor the total measurement. For the same agricultural crop type, athreshold value is provided for the estimate of the amount of nutrientavailable in solution to be absorbed by the roots. The threshold valuesare stored in a memory or storage in the system. Threshold values for aplurality of agricultural crop types may be stored in the memory of thesystem. This enables the system to function for any crop type known tothose of skill in the art. Accordingly, the system may be customized toreceive any number of threshold values.

After receiving the specific crop type, total measurement of nutrientsin the soil, and the estimate of the amount of nutrient available insolution, the system compares these values to their respective thresholdvalues. Based on the comparison, the one more processors of the systemcan provide a combination recommendation to increase the yield of theparticular crop. The combination recommendation may be provided as adisplay.

The system is configured such that recommendations are based, at leastin part, on whether the total measurement falls short of or exceeds thethreshold for a specific crop. When a total measurement falls below athreshold, a recommendation to add nutrients foliarly may be generatedby the one or more processors. Examples of foliar nutrients may include,but are not limited to FP-747, IRON MAID, KNIFE® PLUS, LARGO®, HIGHFIVE, PHLEX-MAG, PHLEX-MAN, ASTRON®, POWER 23-0-0+MO, POWER 24-0-0+MO,PROLIFF-RC, 5.0 CAL, P-48, PAS-PORT, PER “4” MAX, PERK UP, POWER12-0-12, RENAISSANCE, FLORADOX® PRO, POWER 12-6-0, PROTESYN®, POWER0-0-22, POWER 4-4-16, VOLATEX™, PK FIGHT® 0-0-28, POWER 0-22-28 andRAIDER PLUS

When an estimate of the amount of nutrient available in solution fallsbelow a threshold, a recommendation to add nutrients by way of a soilamendment (i.e. fertilizer) may be generated by the one or moreprocessors. Examples of soil amendments include, but are not limited toFP-747, OXYFLOR, PERVADE, RETAIN PRO, DEFENSE-CUZN, DEFENSE-MAG,DEFENSE-MAN, QUAD K 0-0-45, CALPHLEX®, PHLEX-MAG, PHLEX-MAN, BLACKOUT,P-48, THATCH BUSTER, TRICAL® 35-SP, FREE 15, SPIKE, FLORADOX® PRO,VOLATEX™, PROPEL, PROTESYN®, MAXIPLEX, TURGOR® and FIGHT'S ON.

The one or more processors or servers of the present technology are alsoconfigured to rank the recommendations. For each agricultural crop type,the system may contain profiles for each nutrient and combinationrecommendation. These profiles, which are stored in a memory of thesystem, may be ranked by a predetermined priority, in accordance withtheir importance or criticality to yield. The combinationrecommendations that are ultimately displayed by the one or moreprocessors may be further based on these predetermined rankingpriorities.

FIGS. 11(a)-11(c) are a representative example of a printed computerdisplay generated by the present system and methods. FIG. 11(a) providesvarious nutrient information related to a soil sample for an almondcrop. In FIG. 11(a), the total measurement of a nutrient contained inthe soil sample is designated as “Total” in the display, and theestimate of the amount of nutrient available in solution to be absorbedby the roots of an almond crop is designated as “Available.” Thesections labeled “base cation,” “anions,” and “micronutrients” containthe Total and Available values for the various nutrients andmicronutrients contained therein. As shown, the system generates acomparison of the Total and Available values, relative to the threshold.In FIGS. 11(a)-(c) the threshold is referred to as the target. Thevalues for these nutrients are measured in ppm. Nitrogen is analyzedseparately and only an Available measurement is provided. The Availablenitrogen is measured as nitrate (NO₃) and ammonium (NH4), in the soilsample. In addition, an estimate of the monthly nitrogen (ENR) andphosphorus release (EPR) is provided in lbs/acre. This is significant asit takes into account predictable nitrogen and phosphorus concentrationsover time, in addition to the more static nitrogen and phosphorus shownin FIG. 11(b). Finally, the physicality and general information for thesoil sample is provided. This information includes, but is not limitedto Organic Matter (OM) %, Saturation Index, pH, Buffer pH, SolubleSalts, Electrical Conductivity and Excess Carbonates.

Once the system has received the data for the nutrients shown in FIG.11(a), and made the comparisons between the Total data, Available data,and their respective targets, any deficiencies are ranked in accordancewith the predetermined ranking priorities for the agricultural croptype. Those rankings and bulk treatment recommendations are provided inthe “Challenges & Solutions” section of FIG. 11(b). Along with thechallenge presented by the data and comparisons in FIG. 11(a), aproposed solution is generated by the one or more processors, as are afocus ranking of high, medium and low.

FIG. 11(b) also provides an analysis of the Available anion (e.g. HCO₃,NO₃, PO₄, SO₄ and Cl) amounts contained in the soil sample. The systemprovides an ideal percentage for each of the anions, based on the croptype, in order to maximize yield. These percentages are predeterminedand stored in a memory of the system. After the one or more processorsreceives the actual estimates of the Available anions in the soilsample, the system compares the percentage of the actual anions in thesoil, to the ideal values for each of the anions. The one moreprocessors then generates an evaluation regarding whether or not theanion percentage is high, low or optimal, based on the ideal range.

As shown in FIG. 11(b), the system also provides analysis of the cationspresent in the soil sample. In the embodiment shown in FIG. 11(b), Na,K, H, Ca and Mg are evaluated. For the cation concentrations present inthe soil sample, both the Total and Available concentrations are taken.The system then uses the concentrations to calculate a percentage totalof each cation in the soil sample. These percentages are then comparedto ideal percentages, based on predetermined data stored in the system.The one more processors then generate an evaluation regarding whether ornot the anion percentage is high, low or optimal, based on the idealrange.

As shown in FIG. 11(c), the system then generates a final summary of thesoil constituents when compared to its target or threshold. The systemthen generates recommendations for treatment, as shown in the “BulkRecommendations” section of FIG. 11(c). The Bulk Recommendations aregenerated in units of lb/acre, however, this could be provided by thesystem in any unit measurement known in the art. The BulkRecommendations may be applied by an end user over a specified period oftime. By way of example only, the one or more processors may be furtherconfigured to recommend the bulk recommendations in one, two or threeapplications. The one or more processors may generate the recommendationprotocol based on factors such as criticality of the nutrient deficiencyor excess, as indicated by the system. The system would process thesecalculations such that the total bulk recommendation for a particulartreatment could be accomplished within an acceptable period of time. Byway of example only, in FIG. 11 (c), 1096 lbs/acre of Calcium isrecommended. Based on the results calculated by the system, it mayrecommend distributing application of exogenous calcium over threeseparate applications, over a specified time.

In such an embodiment, the system may be programmed with urgencyrankings and cost profiles for each recommendation. The urgency rankingsand cost profiles would be stored in a non-transitory, computer readablemedium and retrieved by the one or more processors of the system at theappropriate time. Based on the urgency, the system may provide threedifferent application options for a specific recommendation (not shown).The system would further be configured such that urgency outweighs cost,in terms of ranking recommendations. In other words, in a certainembodiment, the system would recommend different treatment options, butwill always do it within a period of time such that urgency is notsacrificed because of the cost of a treatment. This ranking systemprogrammed into the one or more processors may provide flexibility forapplying the recommended bulk and foliar treatments within an acceptabletime frame, while reducing cost to the farmer.

Most of the foregoing alternative examples are not mutually exclusive,but may be implemented in various combinations to achieve uniqueadvantages. As these and other variations and combinations of thefeatures discussed above can be utilized without departing from thesubject matter defined by the claims, the foregoing description of theembodiments should be taken by way of illustration rather than by way oflimitation of the subject matter defined by the claims. As an example,the preceding operations do not have to be performed in the preciseorder described above. Rather, various steps can be handled in adifferent order or simultaneously. Steps can also be omitted unlessotherwise stated. In addition, the provision of the examples describedherein, as well as clauses phrased as “such as,” “including” and thelike, should not be interpreted as limiting the subject matter of theclaims to the specific examples; rather, the examples are intended toillustrate only one of many possible embodiments. Further, the samereference numbers in different drawings can identify the same or similarelements.

The invention claimed is:
 1. A method for generating a nutrienttreatment recommendation to increase yield for a specific agriculturalcrop comprising: receiving, by one or more processors, a totalmeasurement of a nutrient contained in a soil sample from the specificagricultural crop in a field; receiving, by the one or more processors,an estimate of an amount of the nutrient available in solution to beabsorbed by roots of the specific agricultural crop from the soilsample; receiving by one or more processors a type of the agriculturalcrop; selecting, by the one or more processors, from a plurality ofthreshold values a first threshold value for the measurement and asecond threshold value for the estimate based on the type of theagricultural crop; comparing, by the one or more processors, themeasurement to the first threshold value; comparing, by the one or moreprocessors, the estimate to the second threshold value; generating, bythe one or more processors, a combination nutrient treatmentrecommendation to increase yield for the specific agricultural cropbased on the comparisons; and providing, by the one or more processors,the combination nutrient treatment recommendation for the specificagricultural crop in the field for display.
 2. The method of claim 1,wherein when the total measurement of the nutrient is less than thefirst threshold value, generating the combination recommendationincludes generating a recommendation to add nutrients foliarly to thespecific agricultural crop.
 3. The method of claim 1, wherein when theestimate is less than the first threshold value, generating thecombination recommendation includes generating a recommendation to addnutrients to the specific agricultural crop as a soil amendment.
 4. Themethod of claim 1, wherein the nutrient is an anion.
 5. The method ofclaim 4, wherein the anion is selected from the group consisting ofphosphorus (P); sulfur (S); chloride (Cl) and bicarbonate (HCO⁻ ₃). 6.The method of claim 1, wherein the nutrient is selected from the groupconsisting of a base cation, nitrogen, and a micronutrient.
 7. Themethod of claim 1, wherein the measurement is received from a sensorsystem.
 8. The method of claim 1, wherein the estimate is received froma sensor system.
 9. The method of claim 1, further comprising: receivinga second total measurement of a second nutrient contained in the soilsample; receiving a second estimate of an amount of the second nutrientavailable to be absorbed by the roots of the specific agricultural crop,and wherein generating the combination nutrient treatment recommendationis further based on the second total measurement and the secondestimate.
 10. The method of claim 9, further comprising: selecting fromthe plurality of threshold values a third threshold value for the secondtotal measurement and a fourth threshold value for the second estimate,and wherein generating the combination nutrient treatment recommendationis further based on a comparison of the third threshold value to thesecond total measurement and a comparison of the fourth threshold valueto the second estimate.
 11. The method of claim 10, wherein thecombination nutrient treatment recommendation includes a specificrecommendation for each of the total measurement, the estimate, thetotal second measurement, and the second estimate.
 12. The method ofclaim 11 further comprising: ranking the specific recommendations basedon predetermined ranking priorities for the agricultural crop type, andwherein generating the combination nutrient treatment recommendation isfurther based on the ranking.
 13. The method of claim 12, furthercomprising selecting the predetermined ranking priorities from a set ofpredetermined ranking priorities based on the agricultural crop type.14. The method of claim 1, wherein each threshold value of the pluralityof threshold values is associated with a particular agricultural croptype such that the plurality of threshold values correspond to aplurality of different agricultural crop types.
 15. A system forgenerating a nutrient treatment recommendation to increase yield for aspecific agricultural crop, the system comprising: memory storing aplurality of threshold values; one or more computing devices having oneor more processors, the one or more processors being configured to:receive a total measurement of a nutrient contained in a soil samplefrom the specific agricultural crop in a field; receive an estimate ofan amount of the nutrient available in solution to be absorbed by rootsof the specific agricultural crop from the soil sample; receive a typeof the specific agricultural crop; select from the plurality ofthreshold values a first threshold value for the measurement and asecond threshold value for the estimate based on the type of thespecific agricultural crop; compare the total measurement to the firstthreshold value; compare the estimate to the second threshold value;generate a combination recommendation to increase yield for the specificagricultural crop based on the comparisons; and provide the combinationnutrient treatment recommendation for the specific agricultural crop inthe field for display.
 16. The system of claim 15, wherein when thetotal measurement of the nutrient is less than the first thresholdvalue, the one or more processors are further configured to generate thecombination recommendation by generating a recommendation to add one ormore nutrients foliarly, to the specific agricultural crop.
 17. Thesystem of claim 15, wherein when the estimate is less than the firstthreshold value, the one or more processors are further configured togenerate the combination recommendation by generating a recommendationto add nutrients to the specific agricultural crop as a soil amendment.18. The system of claim 15, wherein the nutrient is an anion.
 19. Thesystem of claim 18, wherein the anion is selected from the groupconsisting of phosphorus (P); sulfur (S); chloride (Cl) and bicarbonate(HCO3).
 20. The system of claim 15, wherein the nutrient is selectedfrom the group consisting of a base cation, nitrogen, and amicronutrient.
 21. The system of claim 15, wherein the one or moreprocessors are further configured to receive the total measurement froma sensor system.
 22. The system of claim 15, wherein the one or moreprocessors are further configured to receive the estimate from a sensorsystem.
 23. The system of claim 15, wherein the one or more processorsare further configured to: receive a second total measurement of asecond nutrient contained in the soil sample; receive a second estimateof an amount of the second nutrient available to be absorbed by theroots of the specific agricultural crop, and generate the combinationnutrient treatment recommendation further based on the second totalmeasurement and the second estimate.
 24. The system of claim 23, whereinthe one or more processors are configured to: select from the pluralityof threshold values a third threshold value for the second measurementand a fourth threshold value for the second estimate, and generate thecombination recommendation, wherein the recommendation is further basedon a comparison of the third threshold value to the second totalmeasurement and a comparison of the fourth threshold value to the secondestimate.
 25. The system of claim 24, wherein the combination nutrienttreatment recommendation includes a specific recommendation for each ofthe total measurement, the estimate, the second total measurement, andthe second estimate.
 26. The system of claim 25, wherein the one or moreprocessors are further configured to: rank the specific recommendationsbased on predetermined ranking priorities for the agricultural croptype, and generate the combination recommendation further based on theranking.
 27. The system of claim 24, wherein the one or more processorsare further configured to select a predetermined ranking priority from aset of predetermined ranking priorities based on the agricultural croptype.
 28. The system of claim 15, wherein the one or more processors arefurther configured to associate each threshold value of the plurality ofthreshold values with a particular agricultural crop type such that theplurality of threshold values correspond to a plurality of differentagricultural crop types.